{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24934008-0092-4e2d-903f-fc39acf4853c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib as mpl\n",
    "from scipy.stats import lognorm\n",
    "from scipy.stats import beta \n",
    "import scipy as scp\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rc('font',family = 'Times New Roman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec77f6c7-99d1-481b-8a80-5002c6b6cc67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Base_Shear(L):\n",
    "    mm = 1-((L)/(2*R))\n",
    "    thm = np.arctan(mm/(1-mm))\n",
    "    km = (2/(thm)**2)*(1-np.cos(thm))\n",
    "    Wsm = Wtot*(1-mm**2)\n",
    "    Wfm = Wtot-Wsm\n",
    "    Mm=Wsm*km*R+Wfm*R*(1-mm)\n",
    "    Vm = Mm/hi\n",
    "    return Vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29a5a5da-3fe5-4446-b2cf-43fb5bd05ba4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Rotation_Uplift(theta):\n",
    "    Lm2a=Ly+0.01\n",
    "    Lm2b=0\n",
    "    true = 0\n",
    "    while(true==0):\n",
    "        wm2 = theta/((2/Lm2a)-(1/(2*R)))\n",
    "        N2 = 0.55*(Lm2a-Ly)*p*((kuus/p)/(1+kuus*(Lm2a-Ly)/(A*Es)))**(1/3)\n",
    "        Lm2b = (2*wm2*N2/p)**0.5+Ly\n",
    "        if(abs(Lm2a-Lm2b)<0.001):\n",
    "            true = 1\n",
    "        else:\n",
    "            Lm2a=Lm2b\n",
    "    return wm2,Lm2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c679c99-b3dc-4e95-96fa-516545d97fff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def func(x,a,b,c):\n",
    "    return a*np.exp(b*(x-c))/(1+np.exp(b*(x-c)))**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a1a48b37-fe69-4095-a6b4-62efe1401890",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "g = 9.805\n",
    "\n",
    "H = 16.5\n",
    "R = 13.9\n",
    "n = 20\n",
    "bw = 2*np.pi*R/n\n",
    "\n",
    "fillr = 0.95\n",
    "\n",
    "ro = 0.998\n",
    "ph = ro*g*H*fillr\n",
    "p = ph*bw\n",
    "\n",
    "tb = 9.5/1000\n",
    "tw = 17/1000\n",
    "A = bw*tb\n",
    "I = bw*tb**3/12\n",
    "Zp = bw*tb**2/4\n",
    "Es = 210000000\n",
    "v = 0.3 \n",
    "Fys = 235000\n",
    "My = Fys*Zp\n",
    "\n",
    "ros = 7.850 #density of steel\n",
    "Ww = ros*g*H*bw*tw #weight of wall over one side of the strip\n",
    "tr = 31/1000\n",
    "mr = 35\n",
    "Wr = mr*g #weight of roof\n",
    "Wrr = Wr/n\n",
    "mtot = ro*H*fillr*np.pi*R**2\n",
    "Wtot = mtot*g\n",
    "\n",
    "kuu = Es*bw*(tw/R)**1.5/((3*(1-v**2))**0.25)\n",
    "ktt = Es*bw*tw**2*(tw/R)**0.5/(2*(3*(1-v**2))**0.75)\n",
    "ktu = -Es*bw*tw*(tw/R)/(2*(3*(1-v**2))**0.5)\n",
    "\n",
    "kuus = kuu-ktu**2/ktt\n",
    "dtu = -ktu/(ktt*kuu-ktu**2)\n",
    "dtt = kuu/(ktt*kuu-ktu**2)\n",
    "\n",
    "Ly = (6*My/p)**0.5\n",
    "wy = p*Ly**4/(72*Es*I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "990f85b0-5cbc-4a0d-9ae1-93e0140d8f3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45541813614048926"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbc9209a-b25e-4b65-b646-d4c18f313fae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45541813614048926"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3*Fys*tb**2/(2*ro*g*H*fillr))**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6dd937d-a2a4-48fa-940a-c062f8814512",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006107811340919066"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61f1e2b3-5f0b-4885-9f9e-4b305b1cab13",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006107811340919064"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ro*g*fillr*H*Ly**4/(6*Es*tb**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3722ca72-7857-42e0-afb1-2a18d74bd306",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "n = np.linspace(0,145,145, dtype='int')\n",
    "gamma = fillr*H/R\n",
    "vn = np.zeros(len(n))\n",
    "for i in range(len(n)):\n",
    "    vn[i] = np.pi*(2*n[i]+1)/2\n",
    "    \n",
    "I1 = np.zeros(len(n))\n",
    "I1p = np.zeros(len(n))\n",
    "\n",
    "for i in range(len(n)):\n",
    "    I1[i] = scp.special.i1(vn[i]/gamma)\n",
    "    I1p[i] = scp.special.i0(vn[i]/gamma)- scp.special.i1(vn[i]/gamma)/(vn[i]/gamma)\n",
    "\n",
    "mi = np.sum(2*mtot*gamma*(I1/(I1p*vn**3)))\n",
    "hi = H*np.sum((-1)**n*I1*(vn*(-1)**n-1)/(vn**4*I1p))/np.sum((I1/(I1p*vn**3)))\n",
    "Ci = 6.2\n",
    "ro = 998\n",
    "tw = 17.7/1000\n",
    "Es1 = Es*1000\n",
    "Ti = Ci*H*(ro/Es1)**0.5/(tw/R)**0.5\n",
    "ki = 4*np.pi**2*mi/Ti**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "333f726b-c6aa-4ba6-9896-09ebedd4b931",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19762961469710677"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88e3d1c1-498d-497b-88fa-6faf87c39cd1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.583430116180643"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lam1 = 1.8412\n",
    "mc = mtot*2*np.tanh(lam1*gamma)/(lam1*gamma*(lam1**2-1))\n",
    "hc = H*(1+(1-np.cosh(lam1*gamma))/(lam1*gamma*np.sinh(lam1*gamma)))\n",
    "Tc = 2*np.pi*(R/g)**0.5/(lam1*np.tanh(lam1*H/R))**0.5\n",
    "Tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe835d07-e40c-4685-a297-0b6fe63183ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.583430116180643"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7a02142e-638f-4e78-b322-1955b5da0968",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8168.886093302536"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DDBD\n",
    "Vy = Base_Shear(Ly)\n",
    "Vy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e0dd2514-56f8-4dc1-bfd6-e83259c9df83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002910989496898519"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dy = wy*hi/(2*R)+Vy/ki\n",
    "dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c3c3bab5-d49f-40fc-a1be-f48281eb3eab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6636334580444608"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wpl1,Lpl1 = Rotation_Uplift(0.2)\n",
    "Lpl1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1d11aa0-26f8-46f7-aa6d-3ca92252dc1f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Vpl1 = Base_Shear(Lpl1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e103257f-e0f1-43b8-82ba-6c3f5b64bb34",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018253419765255068"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpl1 = wpl1*hi/(2*R)+Vpl1/ki\n",
    "dpl1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b1c00e60-7de5-42af-bda2-f510286a101f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.270520654472635"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = dpl1/dy\n",
    "mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "23d0bf6e-9605-4d28-a793-84e7ecc60b1d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08992628491748185"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ksi = func(mu,0.28,0.24,6)+0.02\n",
    "ksi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0dad5a1b-ebdc-48b4-8cc3-6dd274fb704e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def GMSpectra(Tr):\n",
    "    \n",
    "    T = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]\n",
    "    Sa = np.zeros((len(np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]),30))\n",
    "    Sd = np.zeros((len(np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]),30))\n",
    "\n",
    "    for i in range(len(Sa[0,:])):\n",
    "        Sa[:,i] = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_'+str(i+1)+'.txt')[:,1]\n",
    "        Sd[:,i] = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Displacement/GMRS_'+str(i+1)+'.txt')[:,1]\n",
    "\n",
    "    Sam = np.zeros(len(Sa[:,0]))\n",
    "    Sdm = np.zeros(len(Sa[:,0]))\n",
    "\n",
    "    for i in range(len(Sam)):\n",
    "        Sam[i] = np.median(Sa[i,:])\n",
    "        Sdm[i] = np.median(Sd[i,:])\n",
    "    return T,Sam,Sdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2ea16e5-12d8-4fa0-82aa-4da43d9f2d06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "T,Sam,Sdm = GMSpectra('475')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b15b1da-8085-4378-8b11-f6319be0fdba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute Acceleration Spectrum, $S_A$ [g]')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icon = abs(Tc-T).argmin()\n",
    "plt.plot(T,Sam)\n",
    "plt.xlim(0,6)\n",
    "plt.ylim(0,0.6)\n",
    "plt.plot((Tc,Tc),(0,Sam[icon]),color='k',ls='--')\n",
    "plt.xlabel(r'Period, $T$ [s]',fontsize=12)\n",
    "plt.ylabel(r'Absolute Acceleration Spectrum, $S_A$ [g]',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bfb4e2bf-4e3c-4037-82b2-d56cd64799fd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "586.9973229387336"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vcon = Sam[icon]*g*mc\n",
    "Vcon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6c10c608-eaac-4d73-a9c3-60c2fdc9c762",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Te1 = 0.6\n",
    "#Te2 = 1.34\n",
    "#Te3 = 0.42\n",
    "plt.plot(T,Sdm,label=r'$\\xi_{eq} = $ 0.05')\n",
    "#plt.plot(T,Sdm*(0.1/(0.05+ksi2))**0.5,label=r'$\\theta_{pl} = 0.4rad$')\n",
    "plt.plot(T,Sdm*(0.1/(0.05+ksi))**0.5,label=r'$\\xi_{eq} = $'+str(round(ksi,2)))\n",
    "#plt.plot(T,Sdm*(0.1/(0.05+ksi3))**0.5,label=r'EFB')\n",
    "plt.plot((0,Te1),(dpl1,dpl1),color='k',ls='--')\n",
    "#plt.plot((0,Te2),(dpl2,dpl2),color='k',ls='--')\n",
    "#plt.plot((0,Te3),(dEFB,dEFB),color='k',ls='--')\n",
    "plt.plot((Te1,Te1),(0,dpl1),color='k',ls='--')\n",
    "#plt.plot((Te2,Te2),(0,dpl2),color='k',ls='--')\n",
    "#plt.plot((Te3,Te3),(0,dEFB),color='k',ls='--')\n",
    "#plt.text(0.5,0.043,r'$\\theta_{pl} = 0.4rad$',fontsize=8)\n",
    "#plt.text(0.05,0.012,r'$\\theta_{pl} = 0.2rad$',fontsize=8)\n",
    "#plt.text(0.05,0.015,r'EFB',fontsize=8)\n",
    "plt.xlim(0,2)\n",
    "plt.ylim(0,0.06)\n",
    "plt.legend()\n",
    "plt.xlabel(r'Period, $T$ [s]',fontsize=12)\n",
    "plt.ylabel(r'Relative Displacement Spectrum, $S_D$ [m]',fontsize=12)\n",
    "plt.savefig('C:/Users/rober/Documents/ROSE/PostDoc/Figure15_P4.tiff',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5438f7f-2477-4d6f-bfba-f115d2c06a65",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11834.014504691268\n",
      "11823.499282576657\n",
      "1.0008893494102973\n"
     ]
    }
   ],
   "source": [
    "#Design for 0.2rad\n",
    "keq = (4*np.pi**2*mi/Te1**2)\n",
    "Vbd1 = dpl1*(4*np.pi**2*mi/Te1**2)+Vcon\n",
    "print(Vbd1)\n",
    "print(Vpl1)\n",
    "print(Vbd1/Vpl1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97ea9d35-3367-4346-b958-cb1161d963d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ky = Vy/dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "12cb9234-941d-4774-b989-a2ee0e0362b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.281149106840605"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ty = 2*np.pi*(mi/ky)**0.5\n",
    "Ty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f36150ba-1b0d-450f-8f77-a8f77fde401a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "616159.4554002945"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d628c4dc-20b3-4f85-b5da-c27c53d56bff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "aa8700dc-5524-4f3a-b63b-3a2deefaeea5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff405ebf-f769-4d18-9e1d-6cb536ca4e86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278161e9-d7a5-4558-a879-a2b28adbcf93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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